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CoursesNVIDIA Generative AI LLMs Associate Certification
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NVIDIA Generative AI LLMs Associate Certification

Step into the world of Generative AI with NVIDIA’s LLMs Associate Certification! Tackle real-world challenges through a question-solution approach and gain practical skills in AI, LLMs, and ethical deployment.
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Jeremy Morgan
Innovative Tech Leader, Linux Expert, & Educator
DevOps Pre-Requisite Course
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What you’ll learn

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Description

Prepare to master the future of artificial intelligence with the NVIDIA Generative AI LLMs Associate Certification course—delivered through an engaging, question-solution approach. Each section is shaped around realistic and practical questions, guiding you through structured solutions that mirror real-world challenges faced by AI professionals. This unique methodology ensures you build both foundational understanding and hands-on problem-solving skills essential for applying Generative AI and Large Language Models (LLMs) in today’s rapidly evolving tech landscape.

Powered by NVIDIA—the backbone of AI innovation across industries—this course provides you with the tools, frameworks, and knowledge to leverage cutting-edge AI technologies used by industry giants like OpenAI, Tesla, AWS, and Netflix. Whether you're just starting your AI journey or looking to advance your expertise, you will benefit from a curriculum developed by industry expert Jeremy Morgan, designed to make complex concepts clear and actionable.

What You’ll Learn:

  • Core Machine Learning and AI Foundations: Grasp the key concepts of GenAI and LLMs through practical problem-solving. Topics include RAG (Retrieval-Augmented Generation) architectures, model evaluation, prompt engineering, text vectorization, model selection, and fine-tuning strategies.
  • Data Analysis for GenAI: Learn to analyze and visualize AI data to inform better model selection and interpretation, address data bias, and assess model performance through hands-on questions and solutions.
  • Experimentation with LLMs: Design and implement effective experiments such as A/B testing, ablation studies, and model evaluation, focusing on methods to detect hallucinations and bias, while utilizing robust statistical and human-centered evaluation techniques.
  • GenAI Software Development: Build, deploy, and optimize GenAI solutions for production using best practices in memory management, performance monitoring, Python integration, and scalable software architecture.
  • Trustworthy and Ethical AI: Explore ethical and security considerations in GenAI development. Learn approaches to minimizing bias, ensuring transparency, protecting privacy, and defending against adversarial attacks, with practical solutions for trustworthy AI.

This certification course is designed for learners such as early-career AI professionals, data scientists, developers, and students who want to build foundational skills in large language models (LLMs). It’s ideal if you’re looking to validate your understanding of generative AI concepts and gain hands-on experience with NVIDIA’s AI tools.

As part of the Kodekloud learning community, you can access collaborative forums to ask questions, exchange insights, and support your peers, amplifying your journey toward NVIDIA GenAI certification.

Join us and learn to solve GenAI’s toughest challenges—one question and solution at a time!

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What our students say

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About the instructor

Jeremy Morgan is a Senior Training Architect with endless enthusiasm for learning and sharing knowledge. Since transitioning from an engineering practitioner to an instructor in 2019, he has been dedicated to helping others excel. Passionate about DevOps, Linux, Machine Learning, and Generative AI, Jeremy actively shares his expertise through videos, articles, talks, and his tech blog, which attracts 9,000 daily readers. His work has been featured on Lifehacker, Wired, Hacker News, and Reddit.

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Course Content

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Introduction

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2
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Lesson Content

Module Content

Course Introduction 03:27
How to Reach Out to KodeKloud and Engage with the Community

Core Machine Learning and AI Knowledge

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30
Topics
Lesson Content

Module Content

RAG Architecture Components 01:24
Article: RAG Architecture Components
Python Package for Text Vectorization 00:39
Article: Python Package for Text Vectorization
LLM Evaluation Metric for Factual Accuracy 00:40
Article: LLM Evaluation Metric for Factual Accuracy
Selecting Embedding Models for RAG 00:48
Article: Selecting Embedding Models for RAG
Primary Advantage of RAG vs. Standalone LLM 01:24
Article: Primary Advantage of RAG vs. Standalone LLM
Purpose of Few-Shot Examples in Prompting 01:03
Article: Purpose of Few-Shot Examples in Prompting
Ensuring Chatbot Uses Specific Documentation 00:46
Article: Ensuring Chatbot Uses Specific Documentation
Preparing Datasets for RAG Systems 00:51
Article: Preparing Datasets for RAG Systems
Understanding Attention Mechanism in Transformers 01:52
Article: Understanding Attention Mechanism in Transformers
Prompt Engineering for Text Summarization 00:50
Article: Prompt Engineering for Text Summarization
Balancing Metrics for LLM Production Deployment 00:57
Article: Balancing Metrics for LLM Production Deployment
Selecting Vector Databases for RAG 00:58
Article: Selecting Vector Databases for RAG
Defining Foundation Models (LLMs) 00:49
Article: Defining Foundation Models (LLMs)
Advantage of LoRA vs. Full Fine-Tuning 00:52
Article: Advantage of LoRA vs. Full Fine-Tuning
Python Package for Bag-of-Words Classification 00:47
Article: Python Package for Bag-of-Words Classification

Data Analysis

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Module Content

Model Selection for Imbalanced Data (Accuracy vs. F1) 01:08
Article: Model Selection for Imbalanced Data (Accuracy vs. F1)
Visualizing Transformer Attention Weights 00:36
Article: Visualizing Transformer Attention Weights
Visualizing Demographic Bias in Training Data 00:48
Article: Visualizing Demographic Bias in Training Data
Evaluating Statistical Significance of Fine-Tuning 00:45
Article: Evaluating Statistical Significance of Fine-Tuning
Visualizing Learned Concepts in LLM Representations 00:43
Article: Visualizing Learned Concepts in LLM Representations
Visualizing Correlations Between LLM Evaluation Metrics 00:40
Article: Visualizing Correlations Between LLM Evaluation Metrics
Visualizing Impact of Context Length on LLM Accuracy 00:54
Article: Visualizing Impact of Context Length on LLM Accuracy

Experimentation

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Evaluating LLM Quality Without Single Correct Answer 00:51
Article: Evaluating LLM Quality Without Single Correct Answer
Ensuring Validity in A/B Testing LLM Prompts 00:44
Article: Ensuring Validity in A/B Testing LLM Prompts
Rigorous Experimental Design for Zero-Shot LLM Testing 00:53
Article: Rigorous Experimental Design for Zero-Shot LLM Testing
Reliable Methodology for Evaluating LLM Hallucinations 00:53
Article: Reliable Methodology for Evaluating LLM Hallucinations
Determining Optimal LLM Context Window Size 00:46
Article: Determining Optimal LLM Context Window Size
Evaluating LLM Bias Across Demographic Groups 00:51
Article: Evaluating LLM Bias Across Demographic Groups
Reliable Human Evaluation Protocol for LLM Outputs 00:54
Article: Reliable Human Evaluation Protocol for LLM Outputs
Robust Experimental Control for Measuring Fine-Tuning Impact 00:47
Article: Robust Experimental Control for Measuring Fine-Tuning Impact
Isolating Impact of Training Dataset Size on LLM Performance 00:52
Article: Isolating Impact of Training Dataset Size on LLM Performance
Reliable Baseline for Evaluating LLM Task Performance 00:49
Article: Reliable Baseline for Evaluating LLM Task Performance
Methodologically Sound Ablation Study Approach for LLMs 00:58
Article: Methodologically Sound Ablation Study Approach for LLMs
Rigorous Statistical Comparison of Two LLM Models 00:52
Article: Rigorous Statistical Comparison of Two LLM Models

Software Development

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Module Content

Monitoring High-Throughput LLM Production Performance 00:45
Article: Monitoring High-Throughput LLM Production Performance
Python Package for RAG Vector Database Implementation 00:38
Article: Python Package for RAG Vector Database Implementation
TensorRT Technique for Improving LLM Inference Latency 00:44
Article: TensorRT Technique for Improving LLM Inference Latency
Software Architecture for Variable LLM Workloads 00:47
Article: Software Architecture for Variable LLM Workloads
Ensuring Reliable Performance in Production LLM Deployments 00:41
Article: Ensuring Reliable Performance in Production LLM Deployments
RAG Component for Determining Document Relevance 01:24
Article: RAG Component for Determining Document Relevance
Reducing LLM Memory Footprint During Deployment 00:42
Article: Reducing LLM Memory Footprint During Deployment
Effective Chunking Strategy for RAG Systems 00:47
Article: Effective Chunking Strategy for RAG Systems
Python Library for Semantic Similarity Calculation (LLM Eval) 00:48
Article: Python Library for Semantic Similarity Calculation (LLM Eval)
Software Design Principle for Evolving LLM Applications 00:43
Article: Software Design Principle for Evolving LLM Applications
Managing GPU Memory Constraints for LLMs in Python 00:48
Article: Managing GPU Memory Constraints for LLMs in Python
Failure Handling Pattern for High-Availability LLM Deployment (Python) 00:50
Article: Failure Handling Pattern for High-Availability LLM Deployment (Python)

Trustworthy AI

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Module Content

Minimizing Bias in LLM Fine-Tuning 00:46
Article: Minimizing Bias in LLM Fine-Tuning
RAG's Role in LLM Trustworthiness 00:55
Article: RAG's Role in LLM Trustworthiness
Demonstrating Transparency in LLM Deployment 00:47
Article: Demonstrating Transparency in LLM Deployment
Data Privacy Best Practices for LLMs 00:51
Article: Data Privacy Best Practices for LLMs
Protecting Against Prompt Injection Attacks 00:43
Article: Protecting Against Prompt Injection Attacks

Mock Exam

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Mock Exam
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